Quality Control
jeff karnsomprot
2025-06-05
1 Objective
The goal of this session is to get a clean macula densa dataset to work with.
2 Load Packages
options(future.globals.maxSize = 74 * 1024^3) # 55 GB
getOption("future.globals.maxSize") #59055800320## [1] 794568949764 How many of these are actually MD cells
SO.markers <- FindAllMarkers(SO1, only.pos = TRUE)
SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1)SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1) %>%
slice_head(n = 5) %>%
ungroup() -> top10
DoHeatmap(SO1, features = top10$gene) + NoLegend()
markers.to.plot1 <- c("Lrp2", # PT
"Slc5a12", # PT-S1
"Slc13a3", # PT-S2
"Slc16a9", # PT-S3
"Havcr1", # Injured PT
"Epha7", # dTL
"Cryab", # dTL
"Cdh13", # dTL1
"Slc14a2", # dTL2
"Slc12a1", # TAL
"Umod", # TAL, DCT1
"Egf", # TAL, DCT1,
"Cldn10", # TAL
"Cldn16", # TAL
"Nos1", # MD
"Slc12a3", # DCT
"Pvalb", # DCT1
"Slc8a1", # DCT2, CNT
"Aqp2", # PC
"Slc4a1", # IC-A
"Slc26a4", # IC-B
"Nphs1", # Podo
"Ncam1", # PEC
"Flt1", # Endo
"Emcn", # Glom Endo
"Kdr", # Capillary Endo
"Pdgfrb", # Perivascular
"Pdgfra", # Fib
"Piezo2", # Mesangial
"Acta2", # Mural
"Ptprc", # Immune
"Cd74", # Macrophage
"Skap1", # B/T Cells
"Upk1b", # Uro
"Top2a" # Proliferation
)
DotPlot(SO1,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()
5 Subset out non MD cells.
SO2 <- SCTransform(SO2) %>%
RunPCA() %>%
FindNeighbors(dims = 1:30) %>%
FindClusters() %>%
RunUMAP(dims = 1:30)## Running SCTransform on assay: RNA## vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.## Calculating cell attributes from input UMI matrix: log_umi## Variance stabilizing transformation of count matrix of size 22835 by 11529## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells## There are 3 estimated thetas smaller than 1e-07 - will be set to 1e-07## Found 308 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 22835 genes## Computing corrected count matrix for 22835 genes## Calculating gene attributes## Wall clock passed: Time difference of 22.1136 secs## Determine variable features## Centering data matrix## Place corrected count matrix in counts slot## Warning: The `slot` argument of `SetAssayData()` is deprecated as of SeuratObject 5.0.0.
## ℹ Please use the `layer` argument instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Different cells and/or features from existing assay SCT## Set default assay to SCT## PC_ 1
## Positive: Umod, Egf, Tmem52b, Sult1d1, Klk1, Sostdc1, Wfdc2, Fabp3, Cox6c, Prdx5
## Ly6a, Foxq1, Wfdc15b, Atp1a1, Slc25a5, Krt7, Cox4i1, Cox5b, Ckb, Atp5g1
## Cox8a, Cldn19, Ndufa4, Atp1b1, Ggt1, Atp5b, Chchd10, Gm47708, Cox6b1, Uqcrq
## Negative: Pappa2, Malat1, Gm37376, Mir6236, Zfand5, Robo2, Neat1, Aard, Wwc2, Pde10a
## Itga4, Nadk2, Col4a4, Sdc4, Nos1, Col4a3, Sgms2, S100g, Ptgs2, Etnk1
## Ramp3, Irx1, Itprid2, Hnrnpa2b1, Srrm2, Tmem158, Ranbp3l, Bmp3, Camk2d, Gm24447
## PC_ 2
## Positive: Ubb, Gm22133, Fth1, Mt1, Ldhb, Ftl1, Hspa1a, H3f3b, Fos, Eif1
## Junb, Cd63, Car15, Hspa1b, Prdx1, Rps24, Jun, Fxyd2, Mpc2, Cd9
## Actb, Mt2, Jund, Rpl7, Rps5, Mgst1, Btg2, Rps27a, Tmem213, Itm2b
## Negative: Malat1, Mir6236, Egf, Gm37376, CT010467.1, Umod, mt-Nd5, mt-Nd4l, Tmem52b, Nme7
## mt-Rnr1, Neat1, Slc12a1, Gm24447, mt-Nd6, mt-Nd2, mt-Co1, Atp1b1, Gm28438, Kcnq1ot1
## mt-Nd4, Lars2, Etnk1, mt-Rnr2, Wnk1, Dst, Foxq1, Sult1d1, Slc5a3, Atrx
## PC_ 3
## Positive: Car15, Pappa2, Gm22133, Ldhb, Fth1, Mgst1, Itm2b, Cd63, Mpc2, Cd9
## Aard, Tmem213, Sfrp1, Ftl1, Fxyd2, Tmem59, Bsg, Ramp3, Prdx1, Clu
## Mcub, Tmem158, Irx1, Ppp1r1a, Sdc4, Ndfip1, Tmbim6, Tmem176a, Rpl9, Cldn10
## Negative: Fos, Junb, Jun, Egr1, Btg2, Hspa1b, Hspa1a, Atf3, Zfp36, Ier2
## Fosb, Klf6, Socs3, Klf2, Jund, Dusp1, Dnajb1, Rhob, Gadd45g, Malat1
## Gadd45b, Tsc22d1, Klf4, Ubc, H1f2, Cebpd, Ppp1r15a, Actb, Gm37376, Gm42793
## PC_ 4
## Positive: Mt1, Gm8420, mt-Nd4l, mt-Co1, Gm8885, Rps21, Gm28438, mt-Rnr1, mt-Rnr2, Gm28437
## Apoe, Mt2, Rpl37, mt-Nd5, Rpl38, Gm28037, Gm11808, Gm3511, Rpl37a, Gm28661
## Rps28, Gm29216, Gm11353, mt-Cytb, Gm10925, mt-Nd2, Aebp1, Atp5md, Gm24447, Rpl39
## Negative: Pappa2, Umod, Egf, Aard, Tmem52b, Sult1d1, Tmem158, Car15, Mcub, Ptgs2
## Wwc2, Hsp90b1, Foxq1, Fth1, Dctd, Wnk1, Cd9, Gm22133, Iyd, Ptprz1
## Klk1, Sfrp1, Rpl15-ps2, Cdkn1c, Calr, Sec14l1, Atp1b1, Robo2, Clu, Tmsb4x
## PC_ 5
## Positive: S100g, Gm8420, Tpt1, Rps21, Rps12, Pappa2, Rpl41, Rpl37, Gm8885, Rpl38
## Rpl37a, Mir6236, Rpl23, Rpl39, Gm11808, Gm6136, Actb, Rps27, Rpl23a, Gm3511
## Tmsb10, Rps26, Rpl30, Ppia, Tmem52b, Rpl5-ps1, Gm7206, Gm11353, Rpl34, Rps27a
## Negative: Hspa1a, Klk1, Hspa1b, Gm22133, mt-Rnr2, Car15, Atp1a1, Gm13340, Gm28437, Gm10925
## CT010467.1, mt-Cytb, Itm2b, mt-Nd4, Rpl15-ps2, Cd63, Clcnkb, Fth1, Gm28661, Ftl1
## Jun, Sfrp1, Gm29216, mt-Rnr1, Rpl13, Ptger3, Gpx6, Hspa8, Cldn10, Atp4a## Computing nearest neighbor graph## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11529
## Number of edges: 377768
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7894
## Number of communities: 15
## Elapsed time: 1 seconds## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 08:58:06 UMAP embedding parameters a = 0.9922 b = 1.112## 08:58:06 Read 11529 rows and found 30 numeric columns## 08:58:06 Using Annoy for neighbor search, n_neighbors = 30## 08:58:06 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 08:58:07 Writing NN index file to temp file /var/folders/7n/x74qctp91rng390gx0z9hmd80000gn/T//RtmpuTO9ND/file150301d95057e
## 08:58:07 Searching Annoy index using 1 thread, search_k = 3000
## 08:58:08 Annoy recall = 100%
## 08:58:09 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 08:58:09 Initializing from normalized Laplacian + noise (using RSpectra)
## 08:58:09 Commencing optimization for 200 epochs, with 504322 positive edges
## 08:58:09 Using rng type: pcg
## 08:58:12 Optimization finished
DotPlot(SO2,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale = FALSE,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()## Warning: Could not find Slc5a12 in the default search locations, found in 'RNA'
## assay instead## Warning: Could not find Kdr in the default search locations, found in 'RNA'
## assay instead## Warning: The following requested variables were not found: Slc13a3, Havcr1,
## Pdgfrb, Pdgfra, Piezo2, Upk1b
6 Subset and Recluster
SO3 <- SCTransform(SO3) %>%
RunPCA() %>%
FindNeighbors(dims = 1:10) %>%
FindClusters(resolution = 1) %>%
RunUMAP(dims = 1:30)## Running SCTransform on assay: RNA## vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.## Calculating cell attributes from input UMI matrix: log_umi## Variance stabilizing transformation of count matrix of size 22829 by 11472## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells## There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07## Found 278 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 22829 genes## Computing corrected count matrix for 22829 genes## Calculating gene attributes## Wall clock passed: Time difference of 19.84227 secs## Determine variable features## Centering data matrix## Place corrected count matrix in counts slot## Warning: Different cells and/or features from existing assay SCT## Set default assay to SCT## PC_ 1
## Positive: Umod, Egf, Tmem52b, Sult1d1, Fabp3, Sostdc1, Foxq1, Klk1, Cox6c, Prdx5
## Wfdc2, Ly6a, Wfdc15b, Krt7, Cldn19, Ckb, Slc25a5, Atp1a1, Cox5b, Cox4i1
## Atp5g1, Cox8a, Ggt1, Ndufa4, Atp1b1, Gm47708, Atp5b, Gm44120, Chchd10, Gadd45g
## Negative: Pappa2, Malat1, Zfand5, Gm37376, Aard, Robo2, Mir6236, Wwc2, Pde10a, Neat1
## Itga4, Nadk2, Sdc4, Nos1, S100g, Ramp3, Col4a4, Irx1, Sgms2, Col4a3
## Ptgs2, Tmem158, Itprid2, Bmp3, Ranbp3l, Etnk1, Cdkn1c, Camk2d, Srrm2, Hnrnpa2b1
## PC_ 2
## Positive: Fos, Junb, Jun, Hspa1a, Egr1, Hspa1b, Btg2, Atf3, Zfp36, Ier2
## Fosb, Socs3, Klf6, Klf2, Jund, Dusp1, Dnajb1, H3f3b, Tsc22d1, Rhob
## Gadd45g, Actb, Gadd45b, Mt1, Ubc, Ubb, Cebpd, Klf4, Mt2, H1f2
## Negative: Mir6236, Egf, CT010467.1, Gm37376, mt-Nd5, Malat1, mt-Nd4l, Slc12a1, Pappa2, mt-Rnr1
## mt-Co1, mt-Nd2, Nme7, Etnk1, Umod, mt-Nd4, Wnk1, Gm28438, Sfrp1, Gm24447
## mt-Nd6, Atp1b1, Robo2, Gm13340, Col4a4, mt-Nd1, mt-Rnr2, Kcnq1ot1, Sptbn1, Gm28661
## PC_ 3
## Positive: Gm22133, Fth1, Ldhb, Ubb, Ftl1, Cd63, Car15, Mpc2, Cd9, Prdx1
## Mgst1, Eif1, Fxyd2, Mt1, Tmem213, Rps24, Itm2b, Bsg, Clu, Rpl7
## Rps5, Wfdc2, Rps27a, Mdh1, Rpl9, Rpl11, Aldoa, Ramp3, Rps7, Selenow
## Negative: Malat1, Mir6236, Gm37376, Egf, Fos, CT010467.1, Junb, Umod, Jun, Neat1
## Egr1, Nme7, Tmem52b, Gm24447, Atf3, Btg2, Zfp36, Fosb, mt-Nd5, mt-Rnr1
## mt-Nd6, mt-Nd4l, Klf6, Hspa1b, Kcnq1ot1, Socs3, Slc12a1, Ier2, Lars2, Klf2
## PC_ 4
## Positive: Mt1, Gm8420, mt-Nd4l, mt-Co1, Gm8885, Gm28438, mt-Rnr1, mt-Rnr2, Apoe, Rps21
## Mt2, Gm28437, mt-Nd5, Rpl37, Aebp1, Gm28037, Rpl38, Gm28661, Gpx6, Gm10925
## Gm11808, Gm3511, Gm24447, Gm29216, Fgf9, Rpl37a, mt-Cytb, Egfl6, Atp5md, mt-Nd3
## Negative: Pappa2, Aard, Umod, Tmem52b, Egf, Sult1d1, Tmem158, Mcub, Foxq1, Ptgs2
## Wwc2, Car15, Hsp90b1, Dctd, Iyd, Ptprz1, Cd9, Wnk1, Fth1, Tmsb4x
## Cdkn1c, Ramp3, Gm22133, Rpl15-ps2, Gm44120, Calr, Clu, Sfrp1, Sec14l1, Col4a3
## PC_ 5
## Positive: Hspa1a, Hspa1b, Gm22133, Klk1, Car15, Rpl15-ps2, Gm13340, mt-Rnr2, Atp1a1, CT010467.1
## Rpl13, Jun, Fth1, Itm2b, Ptger3, Cd63, Cldn10, Ftl1, Clcnkb, Hspa8
## Rpl10, Sfrp1, Gm10925, Mt2, Rpl9, Gpx6, mt-Nd4, Mt1, Atp4a, Lpl
## Negative: S100g, Gm8420, Gm8885, Rps21, Rpl37, Rpl41, Actb, Rpl38, Rpl37a, Rps12
## Tmsb10, Gm11808, Rpl39, Gm6136, Tpt1, Gm3511, Igfbp7, Tmem52b, Gm11353, Rps27
## Ppia, Rps26, Rpl5-ps1, Rpl23, Gm9843, Mir6236, Gm4332, Rpl23a, Pappa2, Gm12338## Computing nearest neighbor graph## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11472
## Number of edges: 347067
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7654
## Number of communities: 14
## Elapsed time: 0 seconds## 08:58:44 UMAP embedding parameters a = 0.9922 b = 1.112## 08:58:44 Read 11472 rows and found 30 numeric columns## 08:58:44 Using Annoy for neighbor search, n_neighbors = 30## 08:58:44 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 08:58:44 Writing NN index file to temp file /var/folders/7n/x74qctp91rng390gx0z9hmd80000gn/T//RtmpuTO9ND/file15030766e86ec
## 08:58:44 Searching Annoy index using 1 thread, search_k = 3000
## 08:58:46 Annoy recall = 100%
## 08:58:46 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 08:58:47 Initializing from normalized Laplacian + noise (using RSpectra)
## 08:58:47 Commencing optimization for 200 epochs, with 501470 positive edges
## 08:58:47 Using rng type: pcg
## 08:58:49 Optimization finished
DotPlot(SO3,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale = FALSE,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()## Warning: Could not find Slc5a12 in the default search locations, found in 'RNA'
## assay instead## Warning: Could not find Kdr in the default search locations, found in 'RNA'
## assay instead## Warning: The following requested variables were not found: Slc13a3, Havcr1,
## Pdgfrb, Pdgfra, Piezo2, Upk1b
## Warning: The `slot` argument of `FetchData()` is deprecated as of SeuratObject 5.0.0.
## ℹ Please use the `layer` argument instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
6.1 Repeat Subsetting and reclustering
SO4 <- subset(SO3, idents = c("13"), invert = TRUE)
SO4 <- SCTransform(SO4) %>%
RunPCA() %>%
FindNeighbors(dims = 1:15) %>%
FindClusters(resolution = 1) %>%
RunUMAP(dims = 1:15)## Running SCTransform on assay: RNA## vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.## Calculating cell attributes from input UMI matrix: log_umi## Variance stabilizing transformation of count matrix of size 22741 by 11431## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells## There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07## Found 319 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 22741 genes## Computing corrected count matrix for 22741 genes## Calculating gene attributes## Wall clock passed: Time difference of 21.52544 secs## Determine variable features## Centering data matrix## Place corrected count matrix in counts slot## Warning: Different cells and/or features from existing assay SCT## Set default assay to SCT## PC_ 1
## Positive: Pappa2, Malat1, Gm37376, Zfand5, Mir6236, Aard, Robo2, Wwc2, Neat1, Pde10a
## Itga4, Nadk2, Sdc4, Nos1, Col4a4, S100g, Col4a3, Sgms2, Irx1, Ramp3
## Ptgs2, Etnk1, Tmem158, Itprid2, Srrm2, Hnrnpa2b1, Bmp3, Ranbp3l, Camk2d, Zbtb20
## Negative: Umod, Egf, Tmem52b, Sult1d1, Klk1, Sostdc1, Fabp3, Wfdc2, Cox6c, Prdx5
## Foxq1, Ly6a, Wfdc15b, Atp1a1, Krt7, Slc25a5, Cox5b, Cox4i1, Ckb, Atp5g1
## Cox8a, Cldn19, Ndufa4, Ggt1, Atp1b1, Atp5b, Gm47708, Chchd10, mt-Nd4l, Gm44120
## PC_ 2
## Positive: Malat1, Mir6236, Egf, Gm37376, CT010467.1, Umod, mt-Nd5, Nme7, Tmem52b, Neat1
## mt-Nd4l, mt-Rnr1, Gm24447, Slc12a1, mt-Nd6, Kcnq1ot1, mt-Nd2, mt-Co1, Atp1b1, Gm28438
## Lars2, Etnk1, Wnk1, Dst, mt-Nd4, mt-Rnr2, Foxq1, Sult1d1, Slc5a3, Atrx
## Negative: Gm22133, Fth1, Ubb, Mt1, Ldhb, Ftl1, Eif1, Cd63, Car15, H3f3b
## Prdx1, Mpc2, Fxyd2, Cd9, Rps24, Mgst1, Tmem213, Hspa1a, Rpl7, Mt2
## Itm2b, Rps5, Bsg, Clu, Rps27a, Rps7, Rpl11, Aldoa, Rpl9, Mdh1
## PC_ 3
## Positive: Pappa2, Car15, Itm2b, Mgst1, Cd9, Aard, Gm22133, Ldhb, Sfrp1, Mpc2
## Cd63, Tmem59, Fth1, Tmem158, Bsg, Mcub, Ramp3, Tmem213, Epcam, Prdx1
## Tmbim6, Ly6a, Gm13340, Clu, Hsp90b1, mt-Nd1, Rpl9, Tmem176a, Fxyd2, Ndfip1
## Negative: Fos, Junb, Jun, Hspa1b, Egr1, Btg2, Hspa1a, Zfp36, Atf3, Ier2
## Fosb, Socs3, Klf6, Klf2, Jund, Dnajb1, Dusp1, Gadd45g, Rhob, Tsc22d1
## Gadd45b, Ubc, Cebpd, H3f3b, H1f2, Klf4, H2bc4, Malat1, Actb, Gm42793
## PC_ 4
## Positive: Pappa2, Umod, Aard, Egf, Tmem52b, Sult1d1, Tmem158, Car15, Mcub, Ptgs2
## Wwc2, Hsp90b1, Foxq1, Dctd, Fth1, Wnk1, Gm22133, Iyd, Cd9, Ptprz1
## Sfrp1, Rpl15-ps2, Cdkn1c, Klk1, Sec14l1, Calr, Clu, Ramp3, Robo2, Gm44120
## Negative: Mt1, Gm8420, mt-Nd4l, mt-Co1, Gm8885, Rps21, Gm28438, mt-Rnr1, mt-Rnr2, Gm28437
## Apoe, Mt2, Rpl37, mt-Nd5, Rpl38, Gm28037, Rpl37a, Gm11808, Gm3511, Gm28661
## Aebp1, Gm29216, Gm24447, Gm11353, Gm10925, mt-Cytb, Atp5md, mt-Nd2, Rpl39, Gm6136
## PC_ 5
## Positive: Klk1, Hspa1a, Gm22133, Hspa1b, CT010467.1, mt-Rnr2, Gm13340, Rpl15-ps2, Car15, Atp1a1
## Rpl13, Itm2b, Gm10925, mt-Nd4, Fth1, Cd63, Gm28437, Ptger3, Ftl1, Rpl10
## Hspa8, Apoe, Clcnkb, Gpx6, Gm28661, Cldn10, mt-Cytb, Rpl9, mt-Rnr1, mt-Nd5
## Negative: S100g, Gm8420, Gm8885, Rps21, Pappa2, Rpl41, Rpl37, Rpl38, Actb, Rpl37a
## Tmem52b, Gm11808, Rps12, Tpt1, Gm6136, Rpl39, Gm3511, Ppia, Gm11353, Rps27
## Tmsb10, Aard, Gm9616, Gm7206, Rpl23, Gm9843, Rpl5-ps1, Rps26, Rpl23a, Gm12338## Computing nearest neighbor graph## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11431
## Number of edges: 355957
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7629
## Number of communities: 16
## Elapsed time: 0 seconds## 08:59:23 UMAP embedding parameters a = 0.9922 b = 1.112## 08:59:23 Read 11431 rows and found 15 numeric columns## 08:59:23 Using Annoy for neighbor search, n_neighbors = 30## 08:59:23 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 08:59:23 Writing NN index file to temp file /var/folders/7n/x74qctp91rng390gx0z9hmd80000gn/T//RtmpuTO9ND/file150307287c52f
## 08:59:23 Searching Annoy index using 1 thread, search_k = 3000
## 08:59:25 Annoy recall = 100%
## 08:59:26 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 08:59:26 Initializing from normalized Laplacian + noise (using RSpectra)
## 08:59:26 Commencing optimization for 200 epochs, with 475992 positive edges
## 08:59:26 Using rng type: pcg
## 08:59:29 Optimization finished
DotPlot(SO4,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale = FALSE,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()## Warning: Could not find Slc5a12 in the default search locations, found in 'RNA'
## assay instead## Warning: Could not find Kdr in the default search locations, found in 'RNA'
## assay instead## Warning: The following requested variables were not found: Slc13a3, Havcr1,
## Pdgfrb, Pdgfra, Piezo2, Upk1b
SO4 <- SCTransform(SO4) %>%
RunPCA() %>%
FindNeighbors(dims = 1:15) %>%
FindClusters(resolution = 0.5) %>%
RunUMAP(dims = 1:15)## Running SCTransform on assay: RNA
## vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
## Calculating cell attributes from input UMI matrix: log_umi
## Variance stabilizing transformation of count matrix of size 22741 by 11431
## Model formula is y ~ log_umi
## Get Negative Binomial regression parameters per gene
## Using 2000 genes, 5000 cells
## There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07
## Found 319 outliers - those will be ignored in fitting/regularization step
##
## Second step: Get residuals using fitted parameters for 22741 genes
## Computing corrected count matrix for 22741 genes
## Calculating gene attributes
## Wall clock passed: Time difference of 22.5283 secs
## Determine variable features
## Centering data matrix
## Place corrected count matrix in counts slot
## Set default assay to SCT
## PC_ 1
## Positive: Pappa2, Malat1, Gm37376, Zfand5, Mir6236, Aard, Robo2, Wwc2, Neat1, Pde10a
## Itga4, Nadk2, Sdc4, Nos1, Col4a4, S100g, Col4a3, Sgms2, Irx1, Ramp3
## Ptgs2, Etnk1, Tmem158, Itprid2, Srrm2, Hnrnpa2b1, Bmp3, Ranbp3l, Camk2d, Zbtb20
## Negative: Umod, Egf, Tmem52b, Sult1d1, Klk1, Sostdc1, Fabp3, Wfdc2, Cox6c, Prdx5
## Foxq1, Ly6a, Wfdc15b, Atp1a1, Krt7, Slc25a5, Cox5b, Cox4i1, Ckb, Atp5g1
## Cox8a, Cldn19, Ndufa4, Ggt1, Atp1b1, Atp5b, Gm47708, Chchd10, mt-Nd4l, Gm44120
## PC_ 2
## Positive: Malat1, Mir6236, Egf, Gm37376, CT010467.1, Umod, mt-Nd5, Nme7, Tmem52b, Neat1
## mt-Nd4l, mt-Rnr1, Gm24447, Slc12a1, mt-Nd6, Kcnq1ot1, mt-Nd2, mt-Co1, Atp1b1, Gm28438
## Lars2, Etnk1, Wnk1, Dst, mt-Nd4, mt-Rnr2, Foxq1, Sult1d1, Slc5a3, Atrx
## Negative: Gm22133, Fth1, Ubb, Mt1, Ldhb, Ftl1, Eif1, Cd63, Car15, H3f3b
## Prdx1, Mpc2, Fxyd2, Cd9, Rps24, Mgst1, Tmem213, Hspa1a, Rpl7, Mt2
## Itm2b, Rps5, Bsg, Clu, Rps27a, Rps7, Rpl11, Aldoa, Rpl9, Mdh1
## PC_ 3
## Positive: Pappa2, Car15, Itm2b, Mgst1, Cd9, Aard, Gm22133, Ldhb, Sfrp1, Mpc2
## Cd63, Tmem59, Fth1, Tmem158, Bsg, Mcub, Ramp3, Tmem213, Epcam, Prdx1
## Tmbim6, Ly6a, Gm13340, Clu, Hsp90b1, mt-Nd1, Rpl9, Tmem176a, Fxyd2, Ndfip1
## Negative: Fos, Junb, Jun, Hspa1b, Egr1, Btg2, Hspa1a, Zfp36, Atf3, Ier2
## Fosb, Socs3, Klf6, Klf2, Jund, Dnajb1, Dusp1, Gadd45g, Rhob, Tsc22d1
## Gadd45b, Ubc, Cebpd, H3f3b, H1f2, Klf4, H2bc4, Malat1, Actb, Gm42793
## PC_ 4
## Positive: Pappa2, Umod, Aard, Egf, Tmem52b, Sult1d1, Tmem158, Car15, Mcub, Ptgs2
## Wwc2, Hsp90b1, Foxq1, Dctd, Fth1, Wnk1, Gm22133, Iyd, Cd9, Ptprz1
## Sfrp1, Rpl15-ps2, Cdkn1c, Klk1, Sec14l1, Calr, Clu, Ramp3, Robo2, Gm44120
## Negative: Mt1, Gm8420, mt-Nd4l, mt-Co1, Gm8885, Rps21, Gm28438, mt-Rnr1, mt-Rnr2, Gm28437
## Apoe, Mt2, Rpl37, mt-Nd5, Rpl38, Gm28037, Rpl37a, Gm11808, Gm3511, Gm28661
## Aebp1, Gm29216, Gm24447, Gm11353, Gm10925, mt-Cytb, Atp5md, mt-Nd2, Rpl39, Gm6136
## PC_ 5
## Positive: Klk1, Hspa1a, Gm22133, Hspa1b, CT010467.1, mt-Rnr2, Gm13340, Rpl15-ps2, Car15, Atp1a1
## Rpl13, Itm2b, Gm10925, mt-Nd4, Fth1, Cd63, Gm28437, Ptger3, Ftl1, Rpl10
## Hspa8, Apoe, Clcnkb, Gpx6, Gm28661, Cldn10, mt-Cytb, Rpl9, mt-Rnr1, mt-Nd5
## Negative: S100g, Gm8420, Gm8885, Rps21, Pappa2, Rpl41, Rpl37, Rpl38, Actb, Rpl37a
## Tmem52b, Gm11808, Rps12, Tpt1, Gm6136, Rpl39, Gm3511, Ppia, Gm11353, Rps27
## Tmsb10, Aard, Gm9616, Gm7206, Rpl23, Gm9843, Rpl5-ps1, Rps26, Rpl23a, Gm12338
## Computing nearest neighbor graph
## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11431
## Number of edges: 355957
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8189
## Number of communities: 8
## Elapsed time: 1 seconds## 09:00:02 UMAP embedding parameters a = 0.9922 b = 1.112
## 09:00:02 Read 11431 rows and found 15 numeric columns
## 09:00:02 Using Annoy for neighbor search, n_neighbors = 30
## 09:00:02 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 09:00:02 Writing NN index file to temp file /var/folders/7n/x74qctp91rng390gx0z9hmd80000gn/T//RtmpuTO9ND/file150302fa2cb67
## 09:00:02 Searching Annoy index using 1 thread, search_k = 3000
## 09:00:04 Annoy recall = 100%
## 09:00:05 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:00:05 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:00:05 Commencing optimization for 200 epochs, with 475992 positive edges
## 09:00:05 Using rng type: pcg
## 09:00:08 Optimization finished## Calculating cluster 0
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
## Calculating cluster 6
## Calculating cluster 7SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1) %>%
slice_head(n = 10) %>%
ungroup() -> top10
DoHeatmap(SO4, features = top10$gene) + NoLegend()## Warning in DoHeatmap(SO4, features = top10$gene): The following features were
## omitted as they were not found in the scale.data slot for the SCT assay:
## Gm13194, Gm10248, C430049B03Rik, C2cd4d, 1700022I11Rik, Bmp6, Gm6035, Gchfr,
## Gpc5, Gm3076, Gjb3, Gm13373, Gm7859, Il3ra, Gm3500
